A hybrid Particle Chemical Reaction Optimization for biological image matching based on lateral inhibition

In this paper, a hybrid Particle Chemical Reaction Optimization (PCRO) algorithm and lateral inhibition is proposed to solve the image matching problem. Lateral inhibition has the ability to enhance the characters of image, which can help to improve the accuracy of image matching. In order to overcome the shortcomings of basic Chemical Reaction Optimization (CRO) algorithm, we improve CRO by proposing PCRO which inspired from the thought of Particle Swarm Optimization (PSO). Comparative experimental results in image matching show that our proposed hybrid method performs much better than other bio-inspired algorithms.     

Hybrid bio-inspired lateral inhibition and Imperialist Competitive Algorithm for complicated image matching

Bio-inspired intelligent algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been applied to solve image matching problems. However, due to high computational complexity and premature convergence problems associated with these methods, they have limitations in defining the global optimal matcher efficiently and accurately. To address these problems, we proposed a hybrid bio-inspired optimization approach, coupling the lateral inhibition mechanism and Imperialist Competitive Algorithm (ICA), to solve complicated image matching problems. With the adoption of the lateral inhibition mechanism, the global convergence of conventional ICA algorithms has been greatly improved. We demonstrate the efficiency and feasibility of the proposed approach by extensive comparative experiments.     

Biological image processing via Chaotic Differential Search and lateral inhibition

In this paper, we propose a hybrid biological image processing approach, which is based on Chaotic Differential Search (CDS) algorithm and lateral inhibition (LI) mechanism. We named this hybrid biological image processing approach as LI-CDS. Differential Search (DS) algorithm is a new bio-inspired optimization algorithm mimicking the migration behavior of an organism, and has been successfully used for solution of coordinate system transformation. The property of chaotic variable is integrated into DS to improve its search strategy so that it can escape from the local optimum. Furthermore, lateral inhibition mechanism, which is verified to have good effects on image edge extraction and image enhancement, is employed to pre-process images involved. In this hybrid biological image processing mechanism, our proposed LI-CDS method incorporates both advantages of chaos theory and lateral inhibition mechanism. Series of comparative experimental results by using LI-CDS, DS, CDS and Particle Swarm Optimization (PSO) demonstrate that the proposed LI-CDS performs better than the other three methods.     

Image enhancement algorithm based on improved lateral inhibition network

There is often substantial noise and blurred details in the images captured by cameras. To solve this problem, we propose a novel image enhancement algorithm combined with an improved lateral inhibition network. Firstly, we built a mathematical model of a lateral inhibition network in conjunction with biological visual perception; this model helped to realize enhanced contrast and improved edge definition in images. Secondly, we proposed that the adaptive lateral inhibition coefficient adhere to an exponential distribution thus making the model more flexible and more universal. Finally, we added median filtering and a compensation measure factor to build the framework with high pass filtering functionality thus eliminating image noise and improving edge contrast, addressing problems with blurred image edges. Our experimental results show that our algorithm is able to eliminate noise and the blurring phenomena, and enhance the details of visible and infrared images.     

Multi-option image completion based on semantic matching image

In this paper, we propose a new image completion method based on high-accuracy semantic matching. Existing image completion approaches mainly focus on simple filling, ignoring creativity and accuracy in semantic matching. Our method can complete missing regions with more creative and semantically matching images as well as create a more seamless and consistent completed image.     

Improved keypoint descriptors based on Delaunay triangulation for image matching

In the current keypoint-based image matching methods, not all the keypoints can be reliably matched because of the influence of noise, illumination, and image distortion. In the paper, we propose a novel method based on the Delaunay triangulation to detect and remove possible mismatches. Given previously matched keypoints by a detector in two images, the proposed method utilizes four steps to remove the mismatched point pairs: first, triangulating keypoints in the reference image, and producing a graph consisting of edges that connects the keypoints; second, drawing a graph in the test image by connecting the corresponding points as same as in the reference image; third, detecting abnormal edges in the test image using special constraints; fourth, detecting and removing mismatched point pairs based on the abnormal edges. The experimental results show that the method can detect the mismatches accurately and improve the robustness of current matching algorithms.     

A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching

A novel Chaotic Quantum-behaved Particle Swarm Optimization Based on Lateral Inhibition (LI-CQPSO) is proposed in this paper, which is used to solve complicated image matching problems. As one of the meta-heuristic algorithms inspired by biological behaviors, Particle Swarm Optimization (PSO) has been successfully applied to image matching. However, high computational complexity and premature convergence of PSO are the main drawbacks that limit its further application. In this work, the proposed LI-CQPSO which combines advantages of chaos theory, quantum and lateral inhibition could have better performance. Chaos can guarantee the PSO escaping from local best, quantum can make the traditional PSO with better searching performance as well as having fewer parameters to control, and lateral inhibition is applied to extract the edge of the images by sharpening the spatial profile of excitation in response to a localized stimulus. The detailed process of LI-CQPSO is also given. The effectiveness and feasibility of the proposed algorithm are illustrated in solving image matching problems by series of comparative experiments with PSO, QPSO, and LI-PSO.     

基于相关系数特性的实时匹配算法

提出了一种高精度的快速匹配算法。其基本思想是根据相关系数矩阵的特性来动态改变相关搜索步长的。对于已知目标特性和点扩展函数的情况,求出其理想情况下的相关系数矩阵函数,确定可信阈值和搜索步长;对于不知道目标特性和点扩展函数的情况,通过相关系数矩阵拟合函数或简单变步长法来确定可信阈值和搜索步长。在得到相关系数最大值后,拟合得到亚像素定位精度。与一般相关方法比较,该方法具有定位精度高、稳健性强和运算速度快等优点。     

基于IECM特征匹配的红外弱点目标快速检测算法

提出了一种新的复杂背景下低信噪比红外弱点目标检测算法。根据红外弱点目标在图像中的三维空间特征,从空间认知的角度出发,将三维的灰度分布特征转化为二维的等高线曲线特征,建立红外图像的等高线图(IECM)描述,利用图论中的树结构(等高线树)形式化地表达等高线的空间关系,在此基础上,给出弱点目标检测的等高线树检测准则,同时给出了等高线划分等级的选择方法。理论分析与实验结果表明,该算法具有良好的检测性能,且结构简单,利于硬件实时实现。在信噪比为1.4的情况下,对红外图像序列的检测概率为96.3%。     

Image fusion based on nonsubsampled contourlet transform for infrared and visible light image

In this paper, a new method based on nonsubsampled contourlet transform (NSCT) is proposed to fuse the infrared image and the visible light image, which will produce a new fused image by which the target can be identified more easily. Firstly, two original images were decomposed into low frequency subband coefficients and the bandpass direction subband coefficients by using NSCT. Secondly, the selection of the low frequency subband coefficient and the bandpass direction subband coefficient is discussed in detail. The low frequency subband coefficients are selected based on the regional visual characteristics. For the selection of bandpass direction subband coefficients, this paper proposes a minimum regional cross-gradient method, and the cross-gradient is gained by calculating the gradient between the pixel of bandpass subbands and the adjacent pixel in the fused image of the low-frequency components. Comparison experiments have been performed on different image sets, and experimental results demonstrate that the proposed method performs better in both subjective and objective qualities.     

一种基于词袋模型的大规模图像层次化分组算法

大规模图像集合的自动分组,不仅可以帮助用户快速组织和掌握图像集合的内容,并且是基于图像的三维场景重建应用的前提和重要环节。提出一种基于词袋模型（bag-of-words, BOW）的层次化分组算法,将每幅图像表示为一个超高维视词向量,利用多路量化技术将内容相似的图像量化到同一个节点,从而完成对图像粗略分组。然后,在每组类别里面,对图像的局部特征向量进行逐一匹配,并利用仿射空间不变量的约束条件,去除不可靠特征匹配,得到更为准确可靠的图像相似度度量,从而完成图像的精细分组。实验结果表明:从得到的系统不同阶段图像分组的查准率-查全率（precision-recall）曲线可以看出,精细分组过程可以显著提高粗分组精度,并且在精细分组阶段,使用约束条件比不使用约束还能获得更高的分组精度     

非下采样变换的红外与可见光图像融合

基于非下采样Contourlet变换(NSCT),提出了一种红外和可见光图像融合算法。针对低频子带系数和各带通方向子带系数分别提出了基于图像物理特征的系数加权选择方式与基于区域能量匹配的系数选择方式,即低频基于区域梯度信息、高频基于区域特征因子的加权与选择结合的图像融合算法。实验结果表明:非下采样Contourlet变换具有较快的运算速度,且经非下采样变换后能量更加集中,可提供更多的图像信息。相对于基于像素的图像融合算法,本文的图像融合算法具有更高的融合性能,是一种更适合图像融合的多尺度几何分析(MGA)工具。     

Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain

Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT-PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation.     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

Impacts of appearance parameters on perceived image quality for mobile-phone displays

In order to investigate the perceived image quality (IQ) influenced by different image appearance parameters, a psychophysical experiment was carried out on two mobile-phone displays of different technologies, the in-plane switching (IPS) and the active matrix organic light emitting diode (AMOLED). The test images were generated within a wide range of variations by applying various manipulation methods, involving changes in several kinds of image appearance parameters, i.e. lightness, chroma, hue, and spatial frequency. The perceived attributes of naturalness, colourfulness, brightness, contrast, sharpness, clearness, preference, and overall IQ were visually assessed via categorical judgement method for several application types of test images. The impacts of the manipulation methods on individual attributes have been deeply discussed based on the interrelationships of these attributes, which reveal that the significant parameters towards better overall IQ for mobile-phone displays include high lightness, suitable chroma and lightness frequency distribution, and impartial hue, accordingly better image quality will be accomplished after all these parameters have been optimised at the same time. The achievements of this study would provide constructive instructions on achieving excellent display effects for researchers and manufactories of mobile-phone displays.     

基于高斯混合模型的多区域并行增长图像分割算法

为克服经典区域增长算法中生长规则以及特征选取的困难,提出了基于高斯混合模型的多区域并行区域增长图像分割算法。首先交互选择多个不同区域的种子点,并利用交互式选择的属于每个区域的子块得到混合模型的个数;然后利用最大期望估计混合模型参数作为区域增长的初始参数,并在增长过程中不停地调节模型参数。为了避免初始种子点位置选择对算法性能的影响,采用了多区域并行竞争增长策略。仿真实验获得了较好的分割效果,表明所提出的算法是合理可行的。     

数字图像散斑相关技术的蚁群优化方法

基于蚁群优化方法提出新的数字图像散斑相关算法。该方法模仿了真实蚂蚁从其巢到食物找到最短路径的方式,通过对蚁群优化方法改进,减少迭代次数并改善解的质量。将新的数字图像散斑相关算法应用到计算机模拟的散斑图像和实验获得的散斑图像中,并与广泛使用的Newton-Raphson算法进行了比较。实验结果展示了新算法的精度、可行性和有效性。当数量级为0.01像素,误差离散均方根小于0.002像素。     

A subpixel motion estimation algorithm based on digital correlation for illumination variant and noise image sequences

One of the challenges in practical subpixel motion estimation is how to obtain high accuracy with sufficient robustness to both illumination variations and additive noise. Motivated by the fact that the normalized spatial cross-correlation is invariant to illumination, we introduce a gradient-based subpixel registration method by maximizing the digital correlation (DC) function between the reference and target frames. Such DC function is remodeled with the presence of image noise, yielding that the correlation coefficient is only sensitive to noise standard variance. To fairly suppress the noise corruption, not only the target frame but also the reference one is reformulated into Taylor gradient expression with half but opposite motion vector. The final solution to motion estimates can be approximated into a closed form by reserving first-order coefficient terms of unregistered motion variables. The error trend of approximated solution is discussed. Computer simulations and actual experiments’ results demonstrate the superiority of the proposed method to the LMSE-based method and ordinary DC method when illumination variations and noise exist. Among the experiments, the influences of real subpixel translation value and noise variance degree on accuracy are studied; correspondingly, an optimized iterative idea for big translations and the recommended noise level adaptive to our method are introduced.     

基于Contourlet变换的无参考立体图像质量评价

立体图像的景物生动逼真,给人一种身临其境的全新视觉享受,但在制作、存储和传输过程中往往会产生失真。为了评价立体图像的质量优劣,提出了一种基于轮廓波(Contourlet)变换的无参考立体图像质量评价算法。通过对失真的左、右图像分别进行主成分分析(PCA)融合来生成新的融合图像,并使用基于SSIM(Structural Similarity)立体匹配算法生成视差图和匹配差值图,然后对上述三张图片进行Contourlet变换,再然后使用自定义的高频能量指标并结合边缘强度和信息熵,最后将得到的特征输入支持向量回归(Support Vector Regression,SVR)模型中学习,得出质量评价分数。该方法在德克萨斯大学公布的立体图像库中进行了验证,线性相关系数和斯皮尔曼相关系数在Phase I库中可高达0.957和0.947,在Phase II库中也可高达0.944和0.934,与主观评价吻合度很高,优于最新的一些评价方法。